The present invention relates to removing impulsive noise from corrupted audio signals.
Audio signals are mechanical, magnetic or electric signals representing sound that can be perceived by humans. Audio signals can be recorded using analog or digital techniques. Digital techniques record audio signals on machine readable digital media, such as a compact disk (CD). Analog signals can be recorded, for example, on a phonograph disk or on a magnetic tape.
Audio signals that are generated from analog recordings or received through noisy transmissions are often corrupted by impulsive noise such as crackles and clicks. In the case of old phonograph records, for example, crackles and clicks are generated by dirt, scratches, chemical or biological degradation. Crackles and clicks are different types of impulsive noise. Clicks are high amplitude impulses that are not necessarily additive and may completely corrupt the clean audio signal. Crackles are short, small amplitude impulses that are additively superimposed on the clean audio signal. Although a single crackle lasts only for a small fraction of the period of the sound upon which it is superimposed, an audio signal from an old phonograph record can include many crackles that produce a typical “frying” noise.
Crackles can be removed from the audio signal with a number of techniques. Typically, the crackles are first identified in the audio signal, and next the identified crackles are removed. Some of these techniques assume a particular waveform for crackles. Such crackles are identified in the audio signal based on correlations between the assumed waveform and the audio signal. Other techniques identify crackles in the audio signal using linear prediction. Traditionally, the linear prediction is used to split the audio signal into two parts, where the first part includes the bulk of the clean signal and the second part includes a residue of the clean signal and all the crackles. The crackles are removed from the second part, which is then recombined with the first part. Such linear prediction techniques typically require extensive calculation, such as solving matrix equations, and are often implemented in complex and expensive special hardware.
For digital sound processing, an audio signal is represented by a data sequence that can be generated by periodically sampling an analog audio signal. Typical sampling frequencies are between about 16,000 and 96,000 samples per second. The audio data sequence is often processed by digital filters that suppress or enhance components of the audio signal. For example, speech can be enhanced over background audio using special finite impulse response (“FIR”) filters.
A FIR filter provides a filtered value for a current sample based on the current or other samples in the data sequence, but without using previously generated filtered values. The FIR filter is called a causal filter if it does not use samples that follow the current sample in the data sequence. A FIR filter can be implemented as an adaptive filter that is updated during data processing based on previously processed samples.